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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_GRCm38.p6_data when this report was generated.

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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.11 (b0dd702)

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        These samples were run by seq2science v0.6.0, a tool for easy preprocessing of NGS data.

        Take a look at our docs for info about how to use this report to the fullest.

        Workflow
        atac-seq
        Date
        December 06, 2021
        Project
        seq2science_example
        Contact E-mail
        example@seq2science.com

        Report generated on 2021-12-06, 13:25 based on data in:

        Change sample names:

        Welcome! Not sure where to start?   Watch a tutorial video   (6:06)

        General Statistics

        Showing 27/27 rows and 16/33 columns.
        Sample Name% DuplicationGC content% PF% AdapterInsert Size% Dups% MappedM Total seqs% Proper PairsM Total seqs% AssignedGenome coverageM Genome readsM MT genome readsNumber of PeaksTreatment Redundancy
        DRR138923
        7.2%
        41.6%
        100.0%
        14.2%
        67 bp
        47.2%
        99.8%
        166.5
        99.9%
        65.4
        4.3%
        1.9 X
        104.7
        61.5
        29685
        0.02
        DRR138924
        13.2%
        41.4%
        100.0%
        11.3%
        70 bp
        58.5%
        99.8%
        152.0
        99.9%
        41.3
        8.6%
        1.5 X
        81.9
        69.9
        60201
        0.03
        DRR138925
        9.0%
        40.8%
        100.0%
        12.7%
        67 bp
        59.6%
        99.9%
        125.1
        99.9%
        34.3
        7.0%
        1.1 X
        63.7
        61.3
        41855
        0.02
        DRR138926
        6.4%
        41.1%
        100.0%
        11.3%
        72 bp
        62.6%
        99.8%
        148.0
        99.9%
        36.2
        16.9%
        1.3 X
        70.7
        77.1
        76774
        0.05
        DRR138927
        7.8%
        40.7%
        100.0%
        10.9%
        73 bp
        63.4%
        99.8%
        161.9
        99.8%
        39.2
        12.2%
        1.4 X
        77.0
        84.7
        55647
        0.03
        DRR138928
        4.1%
        42.4%
        100.0%
        12.1%
        69 bp
        49.4%
        99.8%
        140.8
        99.9%
        49.8
        17.9%
        1.5 X
        82.9
        57.7
        103977
        0.06
        DRR138929
        10.6%
        42.1%
        100.0%
        20.2%
        56 bp
        51.0%
        99.8%
        136.3
        99.9%
        48.1
        9.2%
        1.4 X
        81.9
        54.1
        52470
        0.03
        DRR138930
        11.2%
        43.2%
        100.0%
        24.3%
        53 bp
        47.9%
        99.6%
        155.5
        99.9%
        60.6
        14.3%
        1.7 X
        99.3
        55.7
        104330
        0.06
        DRR138931
        9.5%
        41.3%
        100.0%
        14.2%
        66 bp
        56.0%
        99.8%
        140.0
        99.9%
        42.3
        11.0%
        1.4 X
        76.7
        63.0
        50745
        0.03
        DRR138932
        9.2%
        41.0%
        100.0%
        21.7%
        54 bp
        65.1%
        99.9%
        154.2
        99.9%
        38.3
        16.5%
        1.3 X
        72.3
        81.7
        83430
        0.05
        DRR138933
        7.1%
        41.1%
        100.0%
        9.0%
        74 bp
        60.4%
        99.9%
        150.9
        99.9%
        39.6
        17.4%
        1.3 X
        75.3
        75.3
        83829
        0.06
        DRR138934
        6.5%
        41.7%
        100.0%
        12.1%
        66 bp
        54.4%
        99.9%
        142.2
        99.9%
        45.6
        14.0%
        1.4 X
        78.2
        63.8
        72554
        0.04
        DRR138935
        8.4%
        42.6%
        100.0%
        18.5%
        59 bp
        49.2%
        99.8%
        144.4
        99.9%
        50.8
        15.9%
        1.5 X
        88.0
        56.1
        93050
        0.05
        DRR138936
        7.3%
        41.5%
        100.0%
        18.8%
        56 bp
        58.3%
        99.8%
        141.1
        99.9%
        41.1
        14.5%
        1.3 X
        73.4
        67.5
        76679
        0.04
        DRR138937
        8.5%
        41.8%
        100.0%
        18.2%
        57 bp
        58.4%
        99.9%
        173.4
        99.9%
        53.7
        16.2%
        1.6 X
        91.4
        81.8
        103837
        0.06
        DRR138938
        8.6%
        42.3%
        100.0%
        17.6%
        59 bp
        54.2%
        99.9%
        149.4
        99.9%
        50.6
        12.9%
        1.5 X
        84.6
        64.5
        68524
        0.05
        DRR138939
        6.7%
        43.2%
        100.0%
        16.7%
        60 bp
        42.7%
        99.9%
        120.1
        99.9%
        51.7
        10.1%
        1.4 X
        79.1
        40.9
        57006
        0.04
        DRR138940
        8.0%
        43.1%
        100.0%
        21.1%
        57 bp
        42.5%
        99.9%
        141.1
        99.9%
        61.6
        11.6%
        1.7 X
        94.7
        46.1
        70692
        0.05
        DRR138941
        7.8%
        44.4%
        100.0%
        22.9%
        56 bp
        31.3%
        99.9%
        139.1
        99.9%
        76.8
        7.7%
        1.9 X
        107.6
        31.3
        44329
        0.04
        DRR138942
        7.8%
        43.8%
        100.0%
        20.3%
        63 bp
        38.1%
        99.8%
        163.4
        99.9%
        76.3
        10.4%
        2.0 X
        116.3
        46.8
        69840
        0.05
        DRR138943
        5.7%
        44.6%
        100.0%
        22.5%
        59 bp
        27.2%
        99.8%
        123.4
        99.9%
        70.1
        8.0%
        1.7 X
        98.3
        24.9
        45865
        0.04
        DRR138944
        10.1%
        44.3%
        100.0%
        13.1%
        71 bp
        23.6%
        99.8%
        148.6
        99.9%
        86.8
        3.4%
        2.3 X
        128.5
        19.7
        24870
        0.03
        DRR138945
        8.8%
        45.5%
        100.0%
        11.4%
        73 bp
        15.6%
        99.8%
        127.2
        99.9%
        84.6
        2.8%
        2.1 X
        118.6
        8.4
        18441
        0.03
        DRR138946
        10.8%
        45.4%
        100.0%
        10.4%
        75 bp
        17.7%
        99.8%
        121.7
        99.9%
        78.2
        2.8%
        2.0 X
        112.8
        8.6
        18791
        0.03
        DRR138992
        2.4%
        43.3%
        100.0%
        16.9%
        59 bp
        34.6%
        99.9%
        20.3
        99.9%
        9.3
        10.5%
        0.2 X
        13.4
        6.9
        14834
        0.01
        DRR138993
        3.8%
        45.5%
        100.0%
        11.4%
        72 bp
        10.0%
        99.8%
        45.6
        99.9%
        32.2
        2.9%
        0.8 X
        42.6
        3.0
        8760
        0.01
        DRR138994
        4.9%
        45.4%
        100.0%
        10.4%
        75 bp
        11.5%
        99.8%
        48.1
        99.9%
        33.2
        2.9%
        0.8 X
        44.6
        3.4
        9299
        0.01

        Workflow explanation

        Preprocessing of reads was done automatically with workflow tool seq2science v0.5.6. Public samples were downloaded from the Sequence Read Archive with help of the ncbi e-utilities and pysradb. Genome assembly GRCm38.p6 was downloaded with genomepy 0.10.0. The effective genome size was estimated per sample by khmer v2.0 by calculating the number of unique kmers with k being the average read length. Paired-end reads were trimmed with fastp v0.20.1 with default options. Reads were aligned with bwa-mem v0.7.17 with options '-M'. Afterwards, duplicate reads were marked with Picard MarkDuplicates v2.23.8. Mapped reads were removed if they did not have a minimum mapping quality of 30, were a (secondary) multimapper, were a PCR/optical duplicate, aligned inside the ENCODE blacklist or had a template length longer than 150 bp and shorter than 0 bp and finally were tn5 bias shifted by seq2science. General alignment statistics were collected by samtools stats v1.14. Peaks were called with macs2 v2.2.7 with options '--shift -100 --extsize 200 --nomodel --buffer-size 10000' in BAM mode. The effective genome size was estimated by taking the number of unique kmers in the assembly of the same length as the average read length for each sample. Deeptools v3.5.0 was used for the fingerprint, profile, correlation and dendrogram/heatmap plots, where the heatmap was made with options '--distanceBetweenBins 9000 --binSize 1000'. Narrowpeak files of biological replicates belonging to the same condition were merged with fisher's method in macs2. The fraction reads in peak score (frips) was calculated by featurecounts v1.6.4. A peak feature distribution plot and peak localization plot relative to TSS were made with chipseeker. A consensus set of summits was made with gimmemotifs.combine_peaks v0.15.1. The UCSC genome browser was used to visualize and inspect alignment. All summits were extended with 100 bp to get a consensus peakset. Finally, a count table from the consensus peakset with gimmemotifs. Differential accessibility analysis was performed using DESeq2 v1.30.1. To adjust for multiple testing the (default) Benjamini-Hochberg procedure was performed with an FDR cutoff of 0.1 (default is 0.1). Counts were log transformed using the (default) shrinkage estimator apeglm v1.12.0. Quality control metrics were aggregated by MultiQC v1.11.

        Assembly stats

        Genome assembly GRCm38.p6 contains of 140 contigs, with a GC-content of 41.68%, and 2.84% consists of the letter N. The N50-L50 stats are 130694993-9 and the N75-L75 stats are 120129022-15. The genome annotation contains 24831 genes.

        fastp

        fastp An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...)

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotFastp: Filtered ReadsPassed FilterLow QualityToo Many NToo shortDRR138923DRR138925DRR138927DRR138929DRR138931DRR138933DRR138935DRR138937DRR138939DRR138941DRR138943DRR138945DRR138992DRR138994010M20M30M40M50M60M70M80M90M100M110M120M130M140M150M160M170M180M190MCreated with MultiQC
        DRR138942
        Passed Filter: 163381282(100.0%)
        Low Quality: 4578(0.0%)
        Too Many N: 326(0.0%)
        Too short: 0(0.0%)

        Duplication Rates

        Duplication rates of sampled reads.

        Created with Highcharts 5.0.6Duplication levelRead percentChart context menuExport PlotFastp: Duplication Rate246810121416182022242628300%20%40%60%80%100%Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with Highcharts 5.0.6Insert sizeRead percentChart context menuExport PlotFastp: Insert Size Distribution5101520253035404550556065700%1%2%3%4%5%6%7%8%Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with Highcharts 5.0.6Read PositionR1 Before filtering: Sequence QualityChart context menuExport PlotFastp: Sequence Quality2468101214161820222426283032343638404244464850051015202530354045Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with Highcharts 5.0.6Read PositionR1 Before filtering: Base Content PercentChart context menuExport PlotFastp: Read GC Content24681012141618202224262830323436384042444648500%20%40%60%80%100%Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with Highcharts 5.0.6Read PositionR1 Before filtering: Base Content PercentChart context menuExport PlotFastp: Read N Content24681012141618202224262830323436384042444648500%1%2%3%4%5%6%Created with MultiQC

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        Created with Highcharts 5.0.6Insert Size (bp)CountChart context menuExport PlotPicard: Insert Size02550751001251501752002252502753003253503750250000500000750000100000012500001500000Created with MultiQC

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotPicard: Deduplication StatsUnique PairsUnique UnpairedDuplicate Pairs NonopticalDuplicate UnpairedUnmappedDRR138923DRR138925DRR138927DRR138929DRR138931DRR138933DRR138935DRR138937DRR138939DRR138941DRR138943DRR138945DRR138992DRR13899405101520253035404550556065707580859095100Created with MultiQC

        SamTools pre-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        The pre-sieve statistics are quality metrics measured before applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, read length filtering, and tn5 shift.

        Percent Mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotSamtools stats: Alignment ScoresMappedUnmappedDRR138923DRR138925DRR138927DRR138929DRR138931DRR138933DRR138935DRR138937DRR138939DRR138941DRR138943DRR138945DRR138992DRR138994010M20M30M40M50M60M70M80M90M100M110M120M130M140M150M160M170M180M190MCreated with MultiQC

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        Hover over a data point for more information
        Created with Highcharts 5.0.60255075100125150Total sequences
        Created with Highcharts 5.0.60255075100125150Mapped & paired
        Created with Highcharts 5.0.60255075100125150Properly paired
        Created with Highcharts 5.0.60255075100125150Duplicated
        Created with Highcharts 5.0.60255075100125150QC Failed
        Created with Highcharts 5.0.60255075100125150Reads MQ0
        Created with Highcharts 5.0.601k2k3k4k5k6k7k8kMapped bases (CIGAR)
        Created with Highcharts 5.0.601k2k3k4k5k6k7k8kBases Trimmed
        Created with Highcharts 5.0.601k2k3k4k5k6k7k8kDuplicated bases
        Created with Highcharts 5.0.60255075100125150Diff chromosomes
        Created with Highcharts 5.0.60255075100125150Other orientation
        Created with Highcharts 5.0.60255075100125150Inward pairs
        Created with Highcharts 5.0.60255075100125150Outward pairs

        SamTools post-sieve

        Samtools is a suite of programs for interacting with high-throughput sequencing data.

        The post-sieve statistics are quality metrics measured after applying (optional) minimum mapping quality, blacklist removal, mitochondrial read removal, and tn5 shift.

        Percent Mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotSamtools stats: Alignment ScoresMappedDRR138923DRR138925DRR138927DRR138929DRR138931DRR138933DRR138935DRR138937DRR138939DRR138941DRR138943DRR138945DRR138992DRR13899405M10M15M20M25M30M35M40M45M50M55M60M65M70M75M80M85M90M95MCreated with MultiQC

        Alignment metrics

        This module parses the output from samtools stats. All numbers in millions.

        Hover over a data point for more information
        Created with Highcharts 5.0.601020304050607080Total sequences
        Created with Highcharts 5.0.601020304050607080Mapped & paired
        Created with Highcharts 5.0.601020304050607080Properly paired
        Created with Highcharts 5.0.601020304050607080Duplicated
        Created with Highcharts 5.0.601020304050607080QC Failed
        Created with Highcharts 5.0.601020304050607080Reads MQ0
        Created with Highcharts 5.0.60500100015002000250030003500Mapped bases (CIGAR)
        Created with Highcharts 5.0.60500100015002000250030003500Bases Trimmed
        Created with Highcharts 5.0.60500100015002000250030003500Duplicated bases
        Created with Highcharts 5.0.601020304050607080Diff chromosomes
        Created with Highcharts 5.0.601020304050607080Other orientation
        Created with Highcharts 5.0.601020304050607080Inward pairs
        Created with Highcharts 5.0.601020304050607080Outward pairs

        deepTools

        deepTools is a suite of tools to process and analyze deep sequencing data.

        PCA plot

        PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads

        Created with Highcharts 5.0.6PC1PC2Chart context menuExport Plotdeeptools: PCA Plot0.170.17250.1750.17750.180.18250.1850.18750.190.19250.1950.19750.20.20250.205-0.4-0.3-0.2-0.100.10.20.30.4Created with MultiQC

        Fingerprint plot

        Signal fingerprint according to plotFingerprint

        Created with Highcharts 5.0.6rankFraction w.r.t. bin with highest coverageChart context menuExport PlotdeepTools: Fingerprint plot00.050.10.150.20.250.30.350.40.450.50.550.60.650.70.750.80.850.90.95100.20.40.60.81Created with MultiQC

        Read Distribution Profile after Annotation

        Accumulated view of the distribution of sequence reads related to the closest annotated gene. All annotated genes have been normalized to the same size.

        • Green: -2.0Kb upstream of gene to TSS
        • Yellow: TSS to TES
        • Pink: TES to 0.5Kb downstream of gene
        Created with Highcharts 5.0.6OccurrenceChart context menuExport Plotdeeptools: Read Distribution Profile after Annotation-2000-1750-1500-1250-1000-750-500-25002505007501000125015000510152025303540Created with MultiQC

        macs2_frips

        Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.

        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotfeatureCounts: AssignmentsAssignedUnassigned: No FeaturesUnassigned: AmbiguityDRR138923DRR138925DRR138927DRR138929DRR138931DRR138933DRR138935DRR138937DRR138939DRR138941DRR138943DRR138945DRR138992DRR13899402M4M6M8M10M12M14M16M18M20M22M24M26M28M30M32M34M36M38M40M42M44M46MCreated with MultiQC

        deepTools - Spearman correlation heatmap of reads in bins across the genome

        Spearman correlation plot generated by deeptools. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.

        deepTools - Pearson correlation heatmap of reads in bins across the genome

        Pearson correlation plot generated by deeptools. Pearson correlation is a parametric (lots of assumptions, e.g. normality and homoscedasticity) method, and assesses the linearity of the relationship.

        Peak distributions (macs2)

        The distribution of read pileup around 20000 random peaks for each sample. This visualization is a quick and dirty way to check if your peaks look like what you would expect, and what the underlying distribution of different types of peaks is.

        Peak feature distribution (macs2)

        Figure generated by chipseeker

        Distribution of peak locations relative to TSS (macs2)

        Figure generated by chipseeker

        DESeq2 - Sample distance cluster heatmap of counts

        Euclidean distance between samples, based on variance stabilizing transformed counts (RNA: expressed genes, ChIP: bound regions, ATAC: accessible regions). Gives us an overview of similarities and dissimilarities between samples.

        DESeq2 - Spearman correlation cluster heatmap of counts

        Correlation cluster heatmap based on variance stabilizing transformed counts. Spearman correlation is a non-parametric (distribution-free) method, and assesses the monotonicity of the relationship.

        DESeq2 - Pearson correlation cluster heatmap of counts

        Correlation cluster heatmap based on variance stabilizing transformed counts. Pearson correlation is a parametric (lots of assumptions, e.g. normality and homoscedasticity) method, and assesses the linearity of the relationship.

        DESeq2 - MA plot for contrast biological_replicates_st18.5_st7.5

        A MA plot shows the relation between the (normalized) mean counts for each gene/peak, and the log2 fold change between the conditions. Genes/peaks that are significantly differentially expressed are coloured blue. Similarily a volcano plot shows the relation between the log2 fold change between contrasts and their p-value.

        DESeq2 - PCA plot for biological_replicates_st18.5_st7.5

        This PCA plot shows the relation among samples along the two most principal components, coloured by condition. PCA transforms the data from the normalized high dimensions (e.g. 20.000 gene counts, or 100.000 peak expressions) to a low dimension (PC1 and PC2). It does so by maximizing the variance along these two components. Generally you expect there to be more variance between samples from different conditions, than within conditions. This means that you would "expect" similar samples closeby each other on PC1 and PC2.

        Samples & Config

        The samples file used for this run:

        sample assembly biological_replicates descriptive_name
        DRR138923 GRCm38.p6 st7.5 st7.5_rep1
        DRR138924 GRCm38.p6 st7.5 st7.5_rep2
        DRR138925 GRCm38.p6 st7.5 st7.5_rep3
        DRR138926 GRCm38.p6 st8.5 st8.5_rep1
        DRR138927 GRCm38.p6 st8.5 st8.5_rep2
        DRR138928 GRCm38.p6 st8.5 st8.5_rep3
        DRR138929 GRCm38.p6 st9.5 st9.5_rep1
        DRR138930 GRCm38.p6 st9.5 st9.5_rep2
        DRR138931 GRCm38.p6 st9.5 st9.5_rep3
        DRR138932 GRCm38.p6 st10.5 st10.5_rep1
        DRR138933 GRCm38.p6 st10.5 st10.5_rep2
        DRR138934 GRCm38.p6 st10.5 st10.5_rep3
        DRR138935 GRCm38.p6 st12.5 st12.5_rep1
        DRR138936 GRCm38.p6 st12.5 st12.5_rep2
        DRR138937 GRCm38.p6 st12.5 st12.5_rep3
        DRR138938 GRCm38.p6 st14.5 st14.5_rep1
        DRR138939 GRCm38.p6 st14.5 st14.5_rep2
        DRR138992 GRCm38.p6 st14.5 st14.5_rep3
        DRR138940 GRCm38.p6 st14.5 st14.5_rep4
        DRR138941 GRCm38.p6 st16.5 st16.5_rep1
        DRR138942 GRCm38.p6 st16.5 st16.5_rep2
        DRR138943 GRCm38.p6 st16.5 st16.5_rep3
        DRR138944 GRCm38.p6 st18.5 st18.5_rep1
        DRR138993 GRCm38.p6 st18.5 st18.5_rep2
        DRR138945 GRCm38.p6 st18.5 st18.5_rep3
        DRR138946 GRCm38.p6 st18.5 st18.5_rep4
        DRR138994 GRCm38.p6 st18.5 st18.5_rep5

        The config file used for this run: 
        # tab-separated file of the samples
samples: samples.tsv

# pipeline file locations
result_dir: ./results  # where to store results
genome_dir: ./genomes  # where to look for or download the genomes
# fastq_dir: ./results/fastq  # where to look for or download the fastqs


# contact info for multiqc report and trackhub
email: example@seq2science.com

# produce a UCSC trackhub?
create_trackhub: true

# how to handle replicates
biological_replicates: fisher  # change to "keep" to not combine them
technical_replicates: merge    # change to "keep" to not combine them

# which trimmer to use
trimmer: fastp

# which aligner to use
aligner: bwa-mem

# filtering after alignment
remove_blacklist: true
remove_mito: true
tn5_shift: true
min_mapping_quality: 30
only_primary_align: True
max_template_length: 150
remove_dups: true

# peak callers (supported peak callers are macs2, and genrich)
peak_caller:
  macs2:
      --shift -100 --extsize 200 --nomodel --buffer-size 10000
#  genrich:
#      -j -y -D -d 200 -q 0.05

# differential accessibility analysis
contrasts:
  - 'biological_replicates_st18.5_st7.5'

        MultiQC v1.11 (b0dd702) - Written by Phil Ewels, available on GitHub.

        This report uses HighCharts, jQuery, jQuery UI, Bootstrap, FileSaver.js and clipboard.js.